Engineer II – Automation, Development

This is a fully remote position, open to applicants in United States.

📋 Description

• Engage in the research, development, and implementation of machine learning models that enhance cost attribution and optimize cloud efficiency.

• Examine extensive cloud usage and financial datasets.

• Construct and assess machine learning models.

• Collaboratively establish and develop solutions for attributing cloud costs and resource utilization to teams and products.

• Convert business requirements into modeling challenges concerning cost allocation, forecasting, and anomaly detection.

• Design, execute, and assess experiments to validate model precision.

• Collaborate with engineering teams to deploy models in production and oversee their performance and accuracy.

• Share innovative techniques and research insights with the team.

• Provide regular updates on the status of assigned projects.

⛳️ Requirements

• Must be a U.S. citizen or possess a Green Card/permanent resident status.

• Exceptional written and verbal communication skills.

• Proven experience in developing, training, and evaluating machine learning models applied to structured business or financial datasets.

• Proficient in Python and well-versed in popular ML libraries/frameworks, such as PyTorch, TensorFlow, or scikit-learn.

• Familiarity with large-scale datasets and distributed data processing tools, including Spark or Pandas at scale.

• Strong understanding of statistics, probability, and experimental design.

• Knowledge of feature engineering and model evaluation methods, such as precision/recall, RMSE, and cross-validation.

• Experience with SQL and querying relational databases.

• Ability to write clean, well-tested, and reproducible code.

• Strong emphasis on data accuracy and integrity when handling financial and cost datasets.

• Capability to work effectively with cross-functional teams within a global environment.

• Experience with cloud cost/billing data or FinOps practices.

• Understanding of MLOps concepts and tools, including model versioning, monitoring, and continuous integration/continuous deployment (CI/CD) for ML.

• Exposure to big data or streaming platforms, such as Kafka or Spark Streaming.

• Familiarity with AWS, GCP, or Azure for training and deploying machine learning models.

• Experience with resource tagging, cost allocation frameworks, or chargeback/showback systems.

• Knowledge of time-series forecasting techniques.

• Documented experience in leveraging AI technologies to enhance decision-making, streamline workflows, improve efficiency, and achieve business objectives.

🏝️ Benefits

• Leading compensation and equity awards in the market.

• Comprehensive programs for physical and mental wellness.

• Competitive vacation and holiday offerings for optimal rest.

• Paid parental and adoption leave.

• Opportunities for professional development available to all employees, regardless of their position or role.

• Employee Networks, geographic neighborhood groups, and volunteer opportunities to foster connections.

• Dynamic office culture featuring world-class amenities.

• Health insurance coverage.

• 401k plan.

• Paid time off.

• Performance bonuses.

• Opportunities for equity grants.